k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

TL;DR

This paper introduces k-Same-Siamese-GAN, a novel high-resolution facial de-identification method combining k-Same anonymity, GANs, hyperparameter tuning, and mixed precision training for efficient privacy protection.

Contribution

It presents a new GAN-based de-identification approach that automates parameter tuning, enhances efficiency with mixed precision training, and guarantees privacy on high-resolution facial images.

Findings

Effective privacy protection demonstrated on RafD and CelebA datasets.

Improved training speed and reduced memory usage with mixed precision.

Automated hyperparameter tuning enhances model performance.

Abstract

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.